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Abstract

We present a feature-specific imaging system based on the use of structured illumination. The measurements are defined as inner products between the illumination patterns and the object reflectance function, measured on a single photodetector. The illumination patterns are defined using random binary patterns and thus do not employ prior knowledge about the object. Object estimates are generated using L1-norm minimization and gradient-projection sparse reconstruction algorithms. The experimental reconstructions show the feasibility of the proposed approach by using 42% fewer measurements than the object dimensionality.

(a) Example of 32×32 binary sparse object with M=160. (b) L1 reconstructed estimate of the object in (a) for K=350 and K=450 (Simulation data). (c) Example of a 32×32 gray-scale truck object that is “mostly-sparse” in the basis V̿DW. (d) L1 reconstructed estimate of the object in (d) for K=350 and K=450 (Simulation data). (e) Plot of RMSE versus K resulting from applying L1 to the random projection measurements (curves with ‘circles’ and ‘squares’ represent the objects in figs. 4(a) and 4(c) respectively).

(a) Five training objects used for calibration. (b) Plot of first 100 entries of R and Rexp corresponding to the leftmost object in (a). (c) Plot of first 100 entries of R and Rcalib corresponding to the leftmost object in (a).

Experimental results: (a) Two binary objects along with their respective estimates. (b) Two explicitly-sparse objects in V̿PC along with their estimates. (c) Two mostly-sparse truck objects in V̿PC along with their estimates. d) Two mostly-sparse truck objects in V̿DW along with their respective estimates.